Research On Inventory Routing Problem Based On Service Spare Parts Supply Chain

In today’s high-speed economic development, supply chain management hasbecome very important to improve enterprise core competitiveness in the marketeconomy. In current world, the development of the supply chain management hasbeen mature enough and the role of spare parts and services are more and moreimportant in enterprise supply chain. Because the product profit is gradually decline,product after-sales service have been the key benefit increase in the productmanufacturing enterprises. Inventory and transportation routing problem on servicespare parts is the important part of the whole supply chain, through considering thewarehouse construction cost, transportation cost, storage cost and other factors tocoordinate each part in the supply chain network, thus minimizing total operationcost of the service spare parts in the whole supply chain network, and make theenterprise obtain the biggest benefit.This paper focuses on inventory and routing problem based on produce lifecycle service parts supply chain system make further exploration. Through theresearch of the supply chain network, on the basis of the distribution characteristicsof service parts, we consider the inventory costs, transportation costs, constructioncosts, market demand and so on comprehensively to form a spare parts supply chaininventory transportation location model. At the same time, from the globalperspective of the whole supply chain network, consider forward logistics, reverselogistics, recycling and remanufacturing comprehensively to set up service spareparts supply chain inventory routing model.In this article, intelligent algorithm that solve such problem have been made acomprehensive study. According to the characteristics of the inventory routing model,combine the characteristics and advantage of the cellular memory in immunealgorithm to improve the simulated annealing algorithm. We use simulated annealingto search each antibody in antibody group what are produced by immune algorithm, and then gradually cooled and iteration until termination condition is reached.Finally, through example verification, we use simulated annealing algorithm andimmune simulated annealing algorithm to solve the model respectively, reflects thefeasibility that use the immune simulated annealing to deal with this problem. Andimmune simulated annealing algorithm is restricted low by the initial conditions, itcan obtain the optimal solution on the premise of guarantee convergence efficiency.